Comparing remote sensing and ground-based methods of quantifying cover type and carbon storage in an urban forest

Understanding the canopy cover of an urban environment leads to better estimates of carbon storage and more informed management decisions by urban foresters. The most commonly used method for assessing urban forest cover type extent is ground surveys, which can be both timeconsuming and expensive. The analysis of aerial photos is an alternative method that is faster, cheaper, and can cover a larger number of sites, but may be less accurate. The objectives of this paper were (1) to compare three methods of cover type assessment for Los Angeles, CA: handdelineation of aerial photos in ArcMap, supervised classification of aerial photos in ERDAS Imagine, and ground-collected data using the Urban Forest Effects (UFORE) model protocol; (2) to determine how well remote sensing methods estimate carbon storage as predicted by the UFORE model; and (3) to explore the influence of tree diameter and tree density on carbon storage estimates. Four major cover types (bare ground, fine vegetation, coarse vegetation, and impervious surfaces) were determined from 348 plots (0.039 ha each) randomly stratified according to land-use. Hand-delineation was better than supervised classification at predicting ground-based measurements of cover type and UFORE model-predicted carbon storage. Most error in supervised classification resulted from shadow, which was interpreted as unknown cover type. Neither tree diameter or tree density per plot significantly affected the relationship between carbon storage and canopy cover. The efficiency of remote sensing rather than in situ data collection allows urban forest managers the ability to quickly assess a city and plan accordingly while also preserving their often-limited budget.

Determining bridge deck deterioration through the use of 3D photogrammetry

The bridge inspection industry has yet to utilize a rapidly growing technology that shows promise to help improve the inspection process. This thesis investigates the abilities that 3D photogrammetry is capable of providing to the bridge inspector for a number of deterioration mechanisms. The technology can provide information about the surface condition of some bridge components, primarily focusing on the surface defects of a concrete bridge which include cracking, spalling and scaling. Testing was completed using a Canon EOS 7D camera which then processed photos using AgiSoft PhotoScan to align the photos and develop models. Further processing of the models was done using ArcMap in the ArcGIS 10 program to view the digital elevation models of the concrete surface. Several experiments were completed to determine the ability of the technique for the detection of the different defects. The cracks that were able to be resolved in this study were a 1/8 inch crack at a distance of two feet above the surface. 3D photogrammetry was able to be detect a depression of 1 inch wide with 3/16 inch depth which would be sufficient to measure any scaling or spalling that would be required be the inspector. The percentage scaled or spalled was also able to be calculated from the digital elevation models in ArcMap. Different camera factors including the distance from the defects, number of photos and angle, were also investigated to see how each factor affected the capabilities. 3D photogrammetry showed great promise in the detection of scaling or spalling of the concrete bridge surface.

Determining the role of environmental factors and disturbance in the distribution of reed canary grass within wetlands

Reed canary grass (Phalaris arundinacea L.) is an invasive species originally from Europe that has now expanded to a large range within the United States. Reed canary grass possesses a number of traits that allow it to thrive in a wide range of environmental factors, including high rates of sedimentation, bouts of flooding, and high levels of nutrient inputs. Therefore, the goals of our study were to determine if 1) certain types of wetland were more susceptible to Reed canary grass invasion, and 2) disturbances facilitated Reed canary grass invasion. This study was conducted within the Keweenaw Bay Indian Community reservation in the Upper Peninsula of Michigan, in Baraga County. We selected 28 wetlands for analysis. At each wetland, we identified and sampled distinct vegetative communities and their corresponding environmental attributes, which included water table depth, pH, conductivity, calcium and magnesium concentrations, and percent organic matter. Disturbances at each site were catalogued and their severity estimated with the aid of aerial photos. A GIS dataset containing information about the location of Reed canary grass within the study wetlands, the surrounding roads and the level of roadside Reed canary grass invasion was also developed. In all, 287 plant species were identified and classified into 16 communities, which were then further grouped into three broad groupings of wetlands: nonforested graminoid, Sphagnum peatlands, and forested wetlands. The two most common disturbances identified were roads and off-road recreation trails, both occurring at 23 of the 28 sites. Logging activity surrounding the wetlands was the next most common disturbance and was found at 18 of the sites. Occurrence of Reed canary grass was most common in the non-forested graminoid communities. Reed canary grass was very infrequent in forested wetlands, and almost never occurred in the Sphagnum peatlands. Disturbance intensity was the most significant environmental factor in explaining Reed canary grass occurrence within wetlands. Statistically significant relationships were identified at distances of 1000 m, 500 m, and 250 m from studied wetlands, between the level of road development and the severity of Reed canary grass invasion along roadsides. Further analysis revealed a significant relationship between roadside Reed canary grass populations and the level of road development (e.g. paved, graded, and ungraded).